Title: Multiple features fusion for facial expression recognition based on ELM

Authors: Lingzhi Yang; Xiaojuan Ban; Yitong Li; Guang Yang

Addresses: School of Computer and Communication Engineering, University of Science and Technology Beijing, 100083, Beijing, China ' School of Computer and Communication Engineering, University of Science and Technology Beijing, 100083, Beijing, China ' School of Computer and Communication Engineering, University of Science and Technology Beijing, 100083, Beijing, China ' School of Computer and Communication Engineering, University of Science and Technology Beijing, 100083, Beijing, China

Abstract: Traditional facial expression recognition includes a feature extractor and a classifier. In this paper, multiple features fusion approaches for facial expression recognition are proposed to improve the recognition accuracy. We consider a feature level fusion method, serial feature fusion, and decision level fusion, linear opinion pool, to combine multiple features. Local binary patterns, local directional number pattern and edge orientation histograms are used to extract features. Then, extreme learning machine is used as the classifier for expression classification. Experiments on JAFFE and CK+ show the method achieves better results.

Keywords: facial expression recognition; FER; feature extraction; extreme learning machine; ELM; multiple features fusion.

DOI: 10.1504/IJES.2018.091775

International Journal of Embedded Systems, 2018 Vol.10 No.3, pp.181 - 187

Received: 24 Jun 2016
Accepted: 22 Sep 2016
Published online: 16 May 2018 *

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